Compressed Sensing (“CS”) procedures facilitate Magnetic Resonance Image (“MRI”) reconstruction from undersampled k-space data. Existing methods are based on simple regularizers such as Total Variation (“TV”) (see, e.g., Reference 1) or sparsity in the wavelet domain. (See, e.g., Reference 2). However, these handcrafted models are too simple to capture the characteristic structure of complex anatomies. Additionally, application of CS in clinical practice is still challenging due to expensive computations, parameter selection and limited applicability to two-dimensional (“2D”) Cartesian protocols.
Thus, it may be beneficial to provide an exemplary system, method, and computer accessible medium for learning an optimized variational network, which can overcome at least some of the deficiencies described herein above.